Optical Coherence Tomography (OCT) is a technique for performing high-resolution cross-sectional imaging that can provide images of tissue structure on the micron scale in situ and in real time (see for example Huang et al. “Optical Coherence Tomography” Science 254 (5035): 1178 1991). OCT is a method of interferometry that determines the scattering profile of a sample along the OCT beam. Each scattering profile in the depth direction (z) is called an axial scan, or A-scan. Cross-sectional images (B-scans), and by extension 3D volumes, are built up from many A-scans, with the OCT beam moved to a set of transverse (x and y) locations on the sample. One of the principle advantages of OCT is its ability to image the various layers of the retina of the eye. Technical improvements in this modality permit data sets to be obtained in very short times. Due to these high data rates, it behooves the computational power to keep up with the demands of the instrument and of the clinician or physician, who expect polished images or diagnostic metrics to be displayed instantaneously.
One of the important aspects of clinical diagnoses is that of the retinal nerve fiber layer (RNFL). This is the top layer of the retina seen in OCT imaging of the posterior segment of the eye. Embedded within the retina are the axons of the ganglion cells, residing in the layer just below the RNFL. These axons carry the visual signals (action potentials) to the lateral geniculate nucleus and thus the visual cortex. Impairment of either the ganglion cells themselves or their axons in the RNFL results in a diminution of sight, as would be the case with glaucoma, for example. It has been determined that the earliest observable defect as a result of glaucoma is the thinning of the RNFL (see for example Tan et al. 2008). Early detection is imperative if subsequent treatments are to possess any effectiveness.
A vital diagnostic in the ability to discern the presence or progression of glaucoma, is the thickness of the RNFL. A thinner than expected RNFL suggests the presence of disease. Discovery of thinned RNFLs can be accomplished via visual inspection of each and every OCT A-scan in an OCT dataset, which is naturally time consuming. (An OCT dataset is hereinafter defined to be an array of data from an OCT interferometer in any dimensional form. An image can be the same as or a modified version of a subset of a dataset.)
A substantially more effective approach is via automatic computational segmentation and analysis of the layer: with each slice, edges are detected and distances between these edges or boundaries can be measured. Similar types of analyses can be applied to the edges of the various retinal layers including but not limited to the internal limiting membrane (ILM), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), and the outer nuclear layer (ONL), external limiting membrane (ELM), and those layers therein below. A discussion of segmentation techniques may be found in US2008100612 (hereby incorporated by reference).
Other retinal characteristics where segmented OCT data can provide useful information include epiretinal membranes, retinal cysts, central serous retinopathy, macular lesions, drusen, macular degeneration, edema, and lesions, subretinal fluid, and intraretinal exudate (Coats' Disease) among others. In the case of subretinal fluid, it is associated with choroidal neovascularization and RPE elevation, which can and will upset the usefulness of many segmentation algorithms.